Social Media and Social Computing

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Presentations text content in Social Media and Social Computing

Slide1

Social Media and Social Computing

Chapter 1

1

Chapter

1,

Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. 

Slide2

Traditional Media

Broadcast Media: One-to-Many

Communication Media: One-to-One

2

Slide3

Social Media:

Many-to-Many

3

Slide4

Various forms of Social Media

Blog

: Wordpress, blogspot,

LiveJournal

Forum

: Yahoo! Answers,

Epinions

Media Sharing:

Flickr, YouTube, Scribd

Microblogging: Twitter, FourSquare

Social Networking: Facebook, LinkedIn,

OrkutSocial Bookmarking: Del.icio.us

, DiigoWikis: Wikipedia, scholarpedia

, AskDrWiki4

Slide5

Characteristics of Social Media

“Consumers” become “Producers”

Rich User InteractionUser-Generated ContentsCollaborative environment

Collective Wisdom

Long Tail

Broadcast Media

Filter, then Publish

Social Media

Publish, then Filter

5

Slide6

Top 20 Websites at USA

1

Google.com

11

Blogger.com

2

Facebook.com

12

msn.com

3

Yahoo.com

13

Myspace.com

4

YouTube.com

14

Go.com

5

Amazon.com

15

Bing.com

6

Wikipedia.org

16

AOL.com

7

Craigslist.org

17

LinkedIn.com

8

Twitter.com

18

CNN.com

9

Ebay.com

19

Espn.go.com

10

Live.com

20

Wordpress.com

40% of websites are social media sites

6

Slide7

7

Slide8

8

Slide9

Networks and Representation

Graph Representation

Matrix Representation

9

Social Network

: A social structure made of nodes (individuals or organizations) and edges that connect nodes in various relationships like friendship, kinship etc.

Slide10

Basic Concepts

A: the adjacency matrix

V: the set of nodesE: the set of edgesvi: a node v

i

e(v

i

,

v

j): an edge between node vi

and vjN

i: the neighborhood of node vi

di: the degree

of node vigeodesic: a shortest path between two nodes

geodesic distance10

Slide11

Properties of Large-Scale Networks

Networks in social media are typically huge, involving millions of actors and connections.

Large-scale networks in real world demonstrate similar patternsScale-free distributions

Small-world effect

Strong Community Structure

11

Slide12

Scale-free Distributions

Degree distribution in large-scale networks often follows a

power law.

A.k.a.

long tail

distribution,

scale-free

distribution

12

Slide13

log-log plot

Power law distribution becomes a

straight line if plot in a log-log scale

13

Friendship Network in

Flickr

Friendship Network in YouTube

Slide14

Small-World Effect

Six Degrees of Separation”A famous experiment conducted by Travers and

Milgram

(1969)

Subjects were asked to send a chain letter to his acquaintance in order to reach a target person

The average path length is around

5.5

Verified on a planetary-scale IM network of 180 million users (

Leskovec and Horvitz 2008)

The average path length is 6.6

14

Slide15

Diameter

Measures used to calibrate the small world effect

Diameter: the longest shortest path in a network

Average shortest path length

15

The shortest path between two nodes is called

geodesic.

The number of hops in the geodesic is the

geodesic distance.

The geodesic distance between node 1 and node 9 is 4.

The diameter of the network is 5, corresponding to the geodesic distance between nodes 2 and 9.

Slide16

Community Structure

Community

: People in a group interact with each other more frequently than those outside the groupFriends of a friend are likely to be friends as well

Measured by

clustering coefficient:

density of connections among one’s friends

16

Slide17

Clustering Coefficient

d

6=4, N6= {4, 5, 7,8}

k

6

=4 as e(4,5), e(5,7), e(5,8), e(7,8)

C

6

= 4/(4*3/2) = 2/3

Average clustering coefficientC = (C1 + C

2 + … + C

n)/n

C = 0.61 for the left networkIn a random graph, the expected coefficient is 14/(9*8/2) = 0.19.

17

Slide18

Challenges

Scalability

Social networks are often in a scale of millions of nodes and connectionsTraditional Network Analysis often deals with at most hundreds of subjects

Heterogeneity

Various types of entities and interactions are involved

Evolution

Timeliness is emphasized in social media

Collective Intelligence

How to utilize wisdom of crowds in forms of tags, wikis, reviews

EvaluationLack of ground truth, and complete information due to privacy

18

Slide19

Social Computing Tasks

Social Computing: a young and vibrant field

Many new challengesTasksNetwork ModelingCentrality Analysis and Influence Modeling

Community Detection

Classification and Recommendation

Privacy, Spam and Security

19

Slide20

Network Modeling

Large Networks demonstrate statistical patterns:

Small-world effect (e.g., 6 degrees of separation)Power-law distribution (a.k.a. scale-free distribution)

Community structure (high clustering coefficient)

M

odel the network dynamics

Find a mechanism such that the statistical patterns observed in large-scale networks can be reproduced.

Examples: random graph, preferential attachment process, Watts and

Strogatz

modelUsed for simulation to understand network propertiesThomas

Shelling’s famous

simulation: What could cause the segregation of white and black peopleNetwork robustness under attack

Slide21

Comparing Network Models

observations over various

real-word large-scale networks

outcome of a

network model

(Figures borrowed from “

Emergence of Scaling in Random Networks

”)

Slide22

Centrality Analysis and Influence Modeling

Centrality Analysis:

Identify the most important actors or edges

Various criteria

Influence modeling:

How is information diffused?

How does one influence each other?

Related Problems

Viral marketing: word-of-mouth effect

Influence maximization

22

Slide23

Community Detection

A

community is a set of nodes between which the interactions are (relatively) frequent

A.k.a.,

group, cluster, cohesive subgroups, modules

Applications:

Recommendation based communities, Network Compression, Visualization of a huge network

New lines of research in social media

Community Detection in Heterogeneous Networks

Community Evolution in Dynamic Networks

Scalable Community Detection in Large-Scale Networks

23

Slide24

Classification and Recommendation

Common in social media applications

Tag suggestion, Friend/Group Recommendation, Targeting

24

Link

prediction

Network-Based Classification

Slide25

Privacy, Spam and Security

Privacy is a big concern in social media

Facebook, Google buzz often appear in debates about privacyNetFlix

Prize Sequel cancelled due to privacy concern

Simple

annoymization

does not necessarily protect privacy

Spam blog (

splog), spam comments, Fake identity, etc., all requires new techniques

As private information is involved, a secure and trustable system is critical Need to achieve a balance between sharing and privacy

25

Slide26

Book Available at

Morgan & claypool Publishers

Amazon

If you have any comments,

please feel free to contact

:

Lei Tang

, Yahoo! Labs,

ltang@yahoo-inc.com

Huan

Liu

, ASU

huanliu@asu.edu


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